The three human alleles of apolipoprotein E (APOE) differentially influence outcome after CNS injury and affect one's risk of developing Alzheimer's disease (AD). It remains unclear how ApoE isoforms contribute to various AD-related pathological changes (e.g., amyloid plaques and synaptic and neuron loss). Here, we systematically examined whether apoE isoforms (E2, E3, E4) exhibit differential effects on dendritic spine density and morphology in APOE targeted replacement (TR) mice, which lack AD pathological changes. Using Golgi staining, we found age-dependent effects of APOE4 on spine density in the cortex. The APOE4 TR mice had significantly reduced spine density at three independent time points (4 weeks, 3 months, and 1 year, 27.7% Ϯ 7.4%, 24.4% Ϯ 8.6%, and 55.6% Ϯ 10.5%, respectively) compared with APOE3 TR mice and APOE2 TR mice. Additionally, in APOE4 TR mice, shorter spines were evident compared with other APOE TR mice at 1 year. APOE2 TR mice exhibited longer spines as well as significantly increased apical dendritic arborization in the cortex compared with APOE4 and APOE3 TR mice at 4 weeks. However, there were no differences in spine density across APOE genotypes in hippocampus. These findings demonstrate that apoE isoforms differentially affect dendritic complexity and spine formation, suggesting a role for APOE genotypes not only in acute and chronic brain injuries including AD, but also in normal brain functions.

The E4 allele of the Apolipoprotein E (APOE) gene is the strongest genetic risk factor for late-onset Alzheimer's disease (AD), and affects clinical outcomes of chronic and acute brain damages. The mechanisms by which apoE affect diverse diseases and disorders may involve modulation of the glial response to various types of brain damages. We examined glial activation in a mouse model where each of the human APOE alleles are expressed under the endogenous mouse APOE promoter, as well as in APOE knock-out mice. APOE4 mice displayed increased glial activation in response to intracerebroventricular lipopolysaccharide (LPS) compared to APOE2 and APOE3 mice by several measures. There were higher levels of microglia/macrophage, astrocytes, and invading T-cells after LPS injection in APOE4 mice. APOE4 mice also displayed greater and more prolonged increases of cytokines (IL-1β, IL-6, TNF-α) than APOE2 and APOE3 mice. We found that APOE4 mice had greater synaptic protein loss after LPS injection, as measured by three different markers: PSD-95, Drebin, and synaptophysin. In all assays, APOE knock-out mice responded similar to APOE4 mice, suggesting that the apoE4 protein may lack anti-inflammatory characteristics of apoE2 and apoE3. Together, these findings demonstrate that APOE4 predisposes to inflammation, which could contribute to its association with Alzheimer's disease and other disorders.

The Epilepsy Innovation Institute (Ei2) is a new research program of the Epilepsy Foundation designed to be an innovation incubator for epilepsy. Ei2 research areas are selected based on community surveys that ask people impacted by epilepsy what they would like researchers to focus on. In their 2016 survey, unpredictability was selected as a top issue regardless of seizure frequency or severity. In response to this need, Ei2 launched the My Seizure Gauge challenge, with the end goal of creating a personalized seizure advisory system device. Prior to moving forward, Ei2 convened a diverse group of stakeholders from people impacted by epilepsy and clinicians, to device developers and data scientists, to basic science researchers and regulators, for a state of the science assessment on seizure forecasting. From the discussions, it was clear that we are at an exciting crossroads. With the advances in bioengineering, we can utilize digital markers, wearables, and biosensors as parameters for a seizure-forecasting algorithm. There are also over a thousand individuals who have been implanted with ambulatory intracranial EEG recording devices. Pairing up peripheral measurements to brain states could identify new relationships and insights. Another key component is the heterogeneity of the relationships indicating that pooling findings across groups is suboptimal, and that data collection will need to be done on longer time scales to allow for individualization of potential seizure-forecasting algorithms.

Backgroundα-Synuclein aggregates in Lewy bodies and plays a central role in the pathogenesis of a group of neurodegenerative disorders, known as "Synucleinopathies", including Parkinson's disease. Parkin mutations result in loss of parkin E3-ubiquitin ligase activity and cause autosomal recessive early onset parkinsonism.ResultsWe tested how these two genes interact by examining the effects of parkin on post-translational modification of α-Synuclein in gene transfer animal models, using a lentiviral gene delivery system into the striatum of 2-month old male Sprague Dawley rats.Viral expression of wild type α-Synuclein caused accumulation of α-Synuclein and was associated with increased cell death and inflammation. α-Synuclein increased PLK2 levels and GSK-3β activity and increased the levels of phosphorylated α-Synuclein and Tau. Parkin co-expression reduced the levels of phosphorylated α-Synuclein and attenuated cell death and inflammation. Parkin reduced PLK2 levels and increased PP2A activation.ConclusionsThese data suggest that parkin reduces α-Synuclein levels and alters the balance between phosphatase and kinase activities that affect the levels of phosphorylated α-Synuclein. These results indicate novel mechanisms for parkin protection against α-Synuclein-induced toxicity in PD.

Objective: Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Methods: Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms. Results: The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low-risk state, with 14.8% of their time in a high-risk warning state. On average, 69.1% of seizures occurred during high-risk states and 10.5% of seizures occurred in low-risk states. Significance: Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities. K E Y W O R D Scircadian rhythms, epilepsy, mobile health, multiday rhythms, seizure cycles, seizure forecasting | 777 KAROLY et AL.

We describe the longest period of subcutaneous EEG (sqEEG) monitoring to date, in a 35‐year‐old female with refractory epilepsy. Over 230 days, 4791/5520 h of sqEEG were recorded (86%, mean 20.8 [IQR 3.9] hours/day). Using an electronic diary, the patient reported 22 seizures, while automatically‐assisted visual sqEEG review detected 32 seizures. There was substantial agreement between days of reported and recorded seizures (Cohen’s kappa 0.664), although multiple clustered seizures remained undocumented. Circular statistics identified significant sqEEG seizure cycles at circadian (24‐hour) and multidien (5‐day) timescales. Electrographic seizure monitoring and analysis of long‐term seizure cycles are possible with this neurophysiological tool.

Objective: Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Methods: Forecasts based on circadian and multiday seizure cycles were tested pseudo-prospectively using data from 33 app users (mean of 103 seizures per subject). Individual's strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms. Results: The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures. Forecasts using only mobile app diaries allowed users to spend an average of 62.8% of their time in a low-risk state, with 16.6% of their time in a high-risk warning state. On average, 64.5% of seizures occurred during high-risk states and less than 10% of seizures occurred in low-risk states. Significance: Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.

Apolipoprotein E (APOE) genotype affects outcomes of Alzheimer’s Disease and other conditions of brain damage. Using APOE knock-in mice, we have previously shown that APOE- ε4 Targeted Replacement (TR) mice have fewer dendritic spines and reduced branching in cortical neurons. Since dendritic spines are postsynaptic sites of excitatory neurotransmission, we used APOE TR mice to examine whether APOE genotype affected the various elements of the glutamate-glutamine cycle. We found that levels of glutamine synthetase and glutamate uptake transporters were unchanged among the APOE genotypes. However, compared to APOE- ε3 TR mice, APOE-ε4 TR mice had decreased glutaminase levels (18%, p<0.05), suggesting decreased conversion of glutamine to glutamate. APOE-ε4 TR mice also had increased levels of the vesicular glutamate transporter VGLUT1 (20%, p<0.05), suggesting that APOE genotype affects presynaptic terminal composition. To address whether these changes affected normal neurotransmission, we examined the production and metabolism of glutamate and glutamine at 4–5 months and 1 year. Using high frequency 13C/1H nuclear magnetic resonance (NMR) spectroscopy, we found that APOE-ε4 TR mice have decreased production of glutamate and increased levels of glutamine. These factors may contribute to the increased risk of neurodegeneration associated with APOE-ε4, and also act as surrogate markers for AD risk.